Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network
nfrasound, comprehensive nuclear test ban treaty, volcanic eruption, mountain associated waves, neural network, partial least squares, cepstrum An integral part of the Comprehensive Nuclear Test Ban Treaty monitoring is an international infrasonic monitoring network that is capable of detecting and...
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ftfloridainsttec:oai:repository.lib.fit.edu:11141/1737 2023-10-09T21:46:44+02:00 Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network Ham, Fredric M. Leeney, Thomas A. Canady, Heather M. Wheeler, Joseph C. 1999-03-22 http://hdl.handle.net/11141/1737 https://doi.org/10.1117/12.342889 en_US eng Ham, F. M., Leeney, T. A., Canady, H. M., & Wheeler, J. C. (1999). Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network. Proceedings of SPIE - the International Society for Optical Engineering, 3722, 344-356. http://hdl.handle.net/11141/1737 doi:10.1117/12.342889 This published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law. © (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). http://spie.org/publications/journals/guidelines-for-authors#Terms_of_Use Conference Proceeding 1999 ftfloridainsttec https://doi.org/10.1117/12.342889 2023-09-22T09:36:21Z nfrasound, comprehensive nuclear test ban treaty, volcanic eruption, mountain associated waves, neural network, partial least squares, cepstrum An integral part of the Comprehensive Nuclear Test Ban Treaty monitoring is an international infrasonic monitoring network that is capable of detecting and verifying nuclear explosions. Reliable detection of such events must be made from data that may contain other sources of infrasonic phenomena. Infrasonic waves can also result from volcanic eruptions, mountain associated waves, auroral waves, earthquakes, meteors, avalanches, severe weather, quarry blasting, high-speed aircraft, gravity waves, and microbaroms. This paper shows that a feedforward multi-layer neural network discriminator, trained by backpropagation, is capable of distinguishing between two unique infrasonic events recorded from single station recordings with a relatively high degree of accuracy. The two types of infrasonic events used in this study are volcanic eruptions and a set of mountain associated waves recorded at Windless Bight, Antarctica. An important element for the successful classification of infrasonic events is the preprocessing techniques used to form a set of feature vectors that can be used to train and test the neural network. The preprocessing steps used in our analysis for the infrasonic data are similar to those techniques used in speech processing, specifically speech recognition. From the raw time-domain infrasonic data, a set of mel-frequency cepstral coefficients and their associated derivatives for each signal are used to form a set of feature vectors. These feature vectors contain the pertinent characteristics of the data that can be used to classify the events of interest as opposed to using the raw data. A linear analysis was first performed on the feature vector space to determine the best combination of mel-frequency cepstral coefficients and derivatives. Then several simulations were run to distinguish between two different volcanic events, and mountain associated ... Conference Object Antarc* Antarctica The Scholarship Repository of Florida Institute of Technology Windless Bight ENVELOPE(167.667,167.667,-77.700,-77.700) SPIE Proceedings, Applications and Science of Computational Intelligence II 3722 344 356 |
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Open Polar |
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The Scholarship Repository of Florida Institute of Technology |
op_collection_id |
ftfloridainsttec |
language |
English |
description |
nfrasound, comprehensive nuclear test ban treaty, volcanic eruption, mountain associated waves, neural network, partial least squares, cepstrum An integral part of the Comprehensive Nuclear Test Ban Treaty monitoring is an international infrasonic monitoring network that is capable of detecting and verifying nuclear explosions. Reliable detection of such events must be made from data that may contain other sources of infrasonic phenomena. Infrasonic waves can also result from volcanic eruptions, mountain associated waves, auroral waves, earthquakes, meteors, avalanches, severe weather, quarry blasting, high-speed aircraft, gravity waves, and microbaroms. This paper shows that a feedforward multi-layer neural network discriminator, trained by backpropagation, is capable of distinguishing between two unique infrasonic events recorded from single station recordings with a relatively high degree of accuracy. The two types of infrasonic events used in this study are volcanic eruptions and a set of mountain associated waves recorded at Windless Bight, Antarctica. An important element for the successful classification of infrasonic events is the preprocessing techniques used to form a set of feature vectors that can be used to train and test the neural network. The preprocessing steps used in our analysis for the infrasonic data are similar to those techniques used in speech processing, specifically speech recognition. From the raw time-domain infrasonic data, a set of mel-frequency cepstral coefficients and their associated derivatives for each signal are used to form a set of feature vectors. These feature vectors contain the pertinent characteristics of the data that can be used to classify the events of interest as opposed to using the raw data. A linear analysis was first performed on the feature vector space to determine the best combination of mel-frequency cepstral coefficients and derivatives. Then several simulations were run to distinguish between two different volcanic events, and mountain associated ... |
format |
Conference Object |
author |
Ham, Fredric M. Leeney, Thomas A. Canady, Heather M. Wheeler, Joseph C. |
spellingShingle |
Ham, Fredric M. Leeney, Thomas A. Canady, Heather M. Wheeler, Joseph C. Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
author_facet |
Ham, Fredric M. Leeney, Thomas A. Canady, Heather M. Wheeler, Joseph C. |
author_sort |
Ham, Fredric M. |
title |
Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
title_short |
Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
title_full |
Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
title_fullStr |
Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
title_full_unstemmed |
Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
title_sort |
discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network |
publishDate |
1999 |
url |
http://hdl.handle.net/11141/1737 https://doi.org/10.1117/12.342889 |
long_lat |
ENVELOPE(167.667,167.667,-77.700,-77.700) |
geographic |
Windless Bight |
geographic_facet |
Windless Bight |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
Ham, F. M., Leeney, T. A., Canady, H. M., & Wheeler, J. C. (1999). Discrimination of volcano activity and mountain associated waves using infrasonic data and a backpropagation neural network. Proceedings of SPIE - the International Society for Optical Engineering, 3722, 344-356. http://hdl.handle.net/11141/1737 doi:10.1117/12.342889 |
op_rights |
This published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law. © (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). http://spie.org/publications/journals/guidelines-for-authors#Terms_of_Use |
op_doi |
https://doi.org/10.1117/12.342889 |
container_title |
SPIE Proceedings, Applications and Science of Computational Intelligence II |
container_volume |
3722 |
container_start_page |
344 |
op_container_end_page |
356 |
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1779309252083449856 |